piruvari / getting-and-clean-data

setwd("D:/cursos/Getting and Cleaning Data/UCI HAR Dataset/")

#You should create one R script called run_analysis.R that does the following.

#1 Merges the training and the test sets to create one data set. #2 Extracts only the measurements on the mean and standard deviation for each measurement. #3 Uses descriptive activity names to name the activities in the data set #4 Appropriately labels the data set with descriptive variable names. #5 From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

###Read of the all files activityLabel <- read.table("./activity_labels.txt",header=FALSE) features <- read.table("./features.txt",header=FALSE) subject_test <- read.table("./test/subject_test.txt", header=FALSE) X_test <- read.table("./test/X_test.txt", header=FALSE) y_test <- read.table("./test/y_test.txt", header=FALSE) body_acc_x_test <- read.table("./test/Inertial Signals/body_acc_x_test.txt", header=FALSE) body_acc_z_test <- read.table("./test/Inertial Signals/body_acc_z_test.txt", header=FALSE) body_acc_y_test <- read.table("./test/Inertial Signals/body_acc_y_test.txt", header=FALSE) body_gyro_x_test <- read.table("./test/Inertial Signals/body_gyro_x_test.txt", header=FALSE) body_gyro_y_test <- read.table("./test/Inertial Signals/body_gyro_y_test.txt", header=FALSE) body_gyro_z_test <- read.table("./test/Inertial Signals/body_gyro_z_test.txt", header=FALSE) total_acc_x_test <- read.table("./test/Inertial Signals/total_acc_x_test.txt", header=FALSE) total_acc_y_test <- read.table("./test/Inertial Signals/total_acc_y_test.txt", header=FALSE) total_acc_z_test <- read.table("./test/Inertial Signals/total_acc_z_test.txt", header=FALSE) subjectTrain <- read.table("./train/subject_train.txt", header=FALSE) xTrain <- read.table("./train/X_train.txt", header=FALSE) yTrain <- read.table("./train/y_train.txt", header=FALSE) body_acc_x_train <- read.table("./train/Inertial Signals/body_acc_x_train.txt", header=FALSE) body_acc_y_train <- read.table("./train/Inertial Signals/body_acc_y_train.txt", header=FALSE) body_acc_z_train <- read.table("./train/Inertial Signals/body_acc_z_train.txt", header=FALSE) body_gyro_x_train <- read.table("./train/Inertial Signals/body_gyro_x_train.txt", header=FALSE) body_gyro_y_train <- read.table("./train/Inertial Signals/body_gyro_y_train.txt", header=FALSE) body_gyro_z_train <- read.table("./train/Inertial Signals/body_gyro_z_train.txt", header=FALSE) total_acc_x_train <- read.table("./train/Inertial Signals/total_acc_x_train.txt", header=FALSE) total_acc_y_train <- read.table("./train/Inertial Signals/total_acc_y_train.txt", header=FALSE) total_acc_z_train <- read.table("./train/Inertial Signals/total_acc_z_train.txt", header=FALSE)

########1##############################

add the train to test

subject <- rbind(subjectTrain, subject_test) x <- rbind(xTrain, X_test) y <- rbind(yTrain, y_test) ##3-axial acelerómetro body_acc_x <- rbind(body_acc_x_train, body_acc_x_test) body_acc_z <- rbind(body_acc_z_train, body_acc_z_test) body_acc_y <- rbind(body_acc_y_train, body_acc_y_test)

3-axial giroscopio

body_gyro_x <- rbind(body_gyro_x_train, body_gyro_x_test) body_gyro_y <- rbind(body_gyro_y_train, body_gyro_y_test) body_gyro_z <- rbind(body_gyro_z_train, body_gyro_z_test) total_acc_x <- rbind(total_acc_x_train, total_acc_x_test) total_acc_y <- rbind(total_acc_y_train, total_acc_y_test) total_acc_z <- rbind(total_acc_z_train, total_acc_z_test) #############################3##########################

put the name ActivityDesc and replace the id to description of the activity

names(y)<-c("Activity") activityDesc <- factor(y$Activity,levels=activityLabel$V1,labels=activityLabel$V2) activityDesc<-data.frame(activityDesc) ##############################4###############################3

put the friendly name for the 30 voluntaries and name to features

names(subject)<- c("Subject") names(x)<- features$V2

###############################1#########################

merge the subjet activity and features

subjecty <- cbind(subject, activityDesc) Data <- cbind(x, subjecty) #################################2##################################

extract of mean and std

ExtractFeatures<-features$V2[grep("mean\(\)|std\(\)", features$V2)] Extract<- c(as.character(ExtractFeatures), "Subject", "activityDesc" ) Data1 <-subset(Data,select=Extract)

############################################################################ #############result Data1 #############body_acc_x
#############body_acc_z
#############body_acc_y
#############body_gyro_x #############body_gyro_y #############body_gyro_z #############total_acc_x #############total_acc_y #############total_acc_z ##############################5###############################################

create the tydi data

##install.packages('reshape2') ##library(reshape2) ##require(reshape2) meltdata1 <- melt(Data1, id=c("Subject", "activityDesc"), na.rm=TRUE) tidyData <- dcast(meltdata1, Subject + activityDesc ~ variable, mean) write.csv(tidyData, "tidy.csv", row.names=FALSE) write.table(tidyData, "tidy.txt", row.names=FALSE)